Bias is inevitable, especially when it comes to work. Nearly two-thirds of respondents in The Bias Barrier report said they experienced bias in the workplace in 2018. And the sobering statistics continue from there: respondents reported that bias had negative impacts on productivity (68 percent), engagement (70 percent), and on happiness, confidence, and wellbeing (84 percent).
As humans, we can hold a variety of unconscious biases. Many are necessary to daily life, almost intuitive. Some others are less productive and are holdovers from the past, no longer relevant. For example as follows:
These and other types of biases can unconsciously influence our decision-making. Managers might inadvertently hire or promote those who are most like them, make talent selections that align with their preconceived notions, and base their performance evaluations on what they expect to see or have seen most recently.
Organisations are increasingly recognizing that humans are biologically hardwired to operate on instinct and habit, so nonhuman solutions are highly sought-after to mitigate outmoded and problematic biases. For instance, the use of artificial intelligence (AI) in recruitment alone is expected to increase threefold in 2021.
AI is not new, but it has been making interesting strides into talent acquisition, internal mobility, learning and development, and performance management. Some common use cases of AI include:
However, AI is not without its own challenges. The algorithms that drive AI (including the parameters for machine learning applications) are created by humans – and humans have unconscious biases. Until we reach the technology singularity, at which point AI will program itself, this means that AI is also subject to bias.
Many organisations are aware of AI’s flaws and are taking steps to address them. For example, several leading technology companies have announced their use of open-source software tools that can be used to examine bias and fairness in AI models. Furthermore, there is a growing number of AI auditing firms emerging to help address these issues.
AI can provide humans with powerful tools to reduce unconscious bias, but in turn, humans need to design AI with fairness standards in mind and routinely monitor and test algorithms to ensure they do not favour or disadvantage any particular group. This way, we can use human judgment, aided by AI, to reduce both our unconscious biases and inadvertent machine-learning biases.
An increased number of AI tools will continue to emerge, and organisations will become more familiar with behavioural science tools and nudges to help their people make better and more informed talent decisions.